Data Management Glossary
ETL
What is ETL?
ETL stands for Extract, Transform, Load. ETL is a process used to move and prepare data from one system (often raw or messy) into another system (usually for analysis or reporting). It’s a foundational method in data integration and data warehousing. ETL is a term primarily used for structured data management, pioneered by companies like Informatica. A good resource to learn core principles of ETL and data warehousing is The Data Warehouse Institute (TDWI).
Define the ETL Steps
Extract
This is where data is pulled from various sources. Sources can include databases, files, APIs, cloud storage, logs, or unstructured formats like emails or PDFs. The goal is to gather raw data from disparate sources.
Transform
This is where the data is cleaned, formatted, or enriched. This might involve converting dates, removing duplicates, categorizing content, or deriving new fields (e.g., calculating age from birthdate). The goal is to make data usable and consistent.
Load
The transformed data is moved into a destination system, like a data warehouse, data lake, analytics platform, or application. The goal is to store data where it can be accessed, queried, and analyzed by data engineers, data scientist and other data analyst titles.
Why Use ETL?
ETL tools combine data from multiple sources. They are designed to makes data clean, accurate, and analysis-ready. ETL tools typically powers dashboards and business intelligence / analytics tools. A new, cloud-centric approach to data and application integration emerged in the past 10 years that Gartner calls integration platform as a service (iPaaS).
What are Some Common ETL Tools and Examples?
- Traditional: Informatica, Talend, Microsoft SSIS
- Modern cloud-native: Fivetran, Airbyte, dbt (Transform only), AWS Glue, Azure Data Factory, SnapLogic
- For unstructured data: Apache Nifi, Apache Tika, custom Python pipelines (or alternative approaches like Komprise Intelligent Data Management)
Here is a common ETL example – a company wants to analyze sales across multiple stores:
- Extract sales data from each store’s system
- Transform it to a common format and currency
- Load it into a central data warehouse for reporting
What are Some ETL Challenges for Unstructured Data?
Traditional ETL tools were designed for structured data (for example, data in relational databases), where schemas and rules are clearly defined. Unstructured data — such as PDFs, images, audio, and raw text — doesn’t conform to those rules and needs more flexible, intelligent processing.
The core problem with unstructured data is its lack of a common schema. You can’t take a video file, an audio file, or even three video files from three different applications and place them in a tabular format, because they have different contexts and semantics.
Other challenges for ETL tools and unstructured data include:
- Complexity of content AI needs semantics, not just structure; ETL often doesn’t understand meaning.
- AI models often need to work with streaming or near-real-time data whereas ETL is typically batch-based.
- Context AI services (e.g. LLMs or computer vision) require contextual, often multi-modal understanding. ETL doesn’t offer that.
That said, ETL-like processes are useful, especially at the preprocessing and data wrangling stage:
- Extract: Ingest unstructured content from various systems (e.g., files, images, emails).
- Transform: Clean and normalize formats (e.g., convert audio to text, extract text from PDFs).
- Extract metadata (e.g., timestamps, entities, classification).
- Enrich or annotate data (e.g., labeling for training ML models).
- Load: Push processed data to a vector store (e.g., Pinecone, FAISS) or cloud AI service (e.g., OpenAI, Azure AI).
- Store in a data lakehouse (e.g., Databricks, Snowflake) for further analysis or fine-tuning.
Modern ETL Alternative: AI Data Workflows
In many AI data workflows or pipelines, especially for unstructured data, the pattern shifts from ETL to ELT (Extract → Load → Transform) using modern data pipelines. Komprise provides unstructured AI data ingestion capabilities that are a better approach than using traditional ETL tools to address this use case. Learn more about AI Data Workflows. In this scenario, the Transform step is done after data has been copied/migrated/moved to the source where the data is stored, which increasingly is in an Object Storage system like Amazon S3 or Azure Blob. Workflows are iterative and prompt engineering RAG (retrieval-augmented generation) and model training / fine-tuning are part of the process.
Example pipeline for AI and unstructured data
- Ingest PDFs, audio, video → extract with OCR/speech-to-text tools
- Store in object storage or a document DB
- Run AI services (e.g., OpenAI for summaries, classification, embeddings)
- Index in a Global File Index or metadatabase for fast retrieval
- Use in downstream AI apps (chatbots, recommendation systems, etc.)
To make unstructured data usable and searchable for AI, organizations must enrich metadata beyond the basic attributes storage systems provide. This requires creating a global index across storage environments to gain visibility and then applying tags—either manually by knowledgeable users or automatically using AI tools. These enriched tags help identify sensitive data and classify information for specific use cases. Ensuring metadata stays with data during movement is critical, as transferring large unstructured datasets is costly and time-consuming. Therefore, precise metadata-driven classification enables efficient, secure AI data pipelines.
To conclude, traditional ETL tools are not well suited for feeding unstructured data to AI.
Ideal use cases for ETL tools include:
- Preprocessing raw files with custom transformations
- Feeding structured data sources to defined targets
Use cases not well suited for traditional ETL tools include:
- Real-time AI data pipelines, which require a data streaming / data lake architecture
- Semantic understanding & embedding, which require AI-native tooling
RAG, LLMs, semantic search.
ETL tools could play a role for AI data workflows, especially with structured and semi-structured data sources, they should be combined with modern AI services, flexible storage, and unstructured data management solutions.
Data on the Move Discussion: Agentic AI and Unstructured Data Preparation
ETL FAQs
What is ELT, and how is it different from ETL?
ELT stands for Extract, Load, Transform. It reverses the order of the last two steps: data is extracted from source systems and loaded into a destination first, then transformed in place using the compute power of the destination platform.
ETL transforms data before it moves, which suited older data warehouses with limited processing capacity. ELT emerged with cloud-native platforms such as Databricks and Snowflake, where it is cheaper and faster to load raw data and run transformations inside the platform than to pre-process it externally.
For unstructured data, neither approach solves the core problem. ELT still requires moving all raw files to the destination before any structure or schema can be applied. At petabyte scale across NAS environments, that means weeks of transfer time and significant compute cost before a single file is queryable. Komprise takes a different path: it builds a Global Metadatabase in place across all file and object storage environments, enriches files with metadata without moving them, and delivers only the right subset of data to AI tools and lakehouses. The transform step happens before the load step, and most data never needs to move at all.
Good question, and it’s directly relevant to the FAQ we drafted earlier. Here’s the distinction:
- ETL (Extract, Transform, Load) transforms data before it reaches the destination. You pull raw data from the source, clean and restructure it externally, then load only the processed output into the target system. This made sense when destination systems (traditional data warehouses) had limited compute and couldn’t do heavy processing themselves.
- ELT (Extract, Load, Transform) flips the last two steps. You pull raw data and load it into the destination first, then run transformations in place using the destination platform’s own compute. This became practical with cloud-native platforms like Snowflake and Databricks, which have abundant processing power and can handle transformation at scale cheaply.
For unstructured data, neither solves the core problem. Here’s why:
Both ETL and ELT assume you move all the raw data first, then figure out structure and schema. For structured data that’s manageable. For unstructured data at petabyte scale across NAS environments, that assumption breaks down in three ways:
- First, the volume problem. Moving petabytes of files from on-premises NAS to a cloud destination takes weeks to months and costs significant network and storage dollars, regardless of whether you transform before or after the move.
- Second, the schema problem. ETL transforms data into a defined schema before loading. ELT loads raw data and transforms it after. But unstructured files have no enforced schema to begin with. Loading a PDF, DICOM file, or engineering drawing as a raw blob into Snowflake or Databricks doesn’t make it queryable. You still have to do the hard work of extracting meaning from the content, and neither ETL nor ELT tooling does that natively.
- Third, the governance problem. Both approaches move data before sensitive content is identified. That means PII, PHI, and confidential files travel to the destination before any detection or filtering runs. For regulated industries that is a compliance risk, not just an efficiency problem.
The Komprise difference is that classification, metadata enrichment, and sensitive data detection happen before any data moves, against files sitting in place across NAS and object storage. Only the right subset moves, already enriched and governed.
Why do traditional ETL tools fall short for AI data pipelines?
Traditional ETL tools were built to move structured data between relational systems with defined schemas. Unstructured data, including files, images, documents, and medical records, has no enforced schema, no consistent format, and no built-in context for AI to work with.
Three problems surface when enterprises try to use ETL for unstructured AI data preparation.
- First, ETL tools copy everything: they move raw data from source to destination regardless of whether it is relevant, creating unnecessary cost and storage overhead in the destination.
- Second, they do not enrich. ETL tools pass through whatever metadata already exists, which for most files is limited to filename, size, and timestamps. AI pipelines need content-level classification, sensitive data detection, and domain-specific tags that ETL cannot produce.
- Third, they do not scale for NAS. Moving petabytes of file data from on-premises NAS across to a lakehouse can take months and requires separate tooling for each storage vendor. ETL connectors are not built for that environment.
Komprise addresses all three: Kompise indexes data in place across multi-vendor NAS and cloud storage, enriches it with KAPPA data services, filters out irrelevant and sensitive files before they reach AI pipelines, and transfers only what is needed using Intelligent AI Ingest at 2x standard transfer speeds.
When should an enterprise use ETL tools, and when does unstructured data management make more sense?
ETL tools are the right choice when the data source is structured or semi-structured (relational databases, CSV files, JSON feeds), the destination schema is well-defined, the data volume is manageable, and the goal is repeatable batch processing into a data warehouse or BI platform. For those use cases, tools such as Informatica, Fivetran, and AWS Glue are mature, well-supported, and appropriate.
Unstructured data management is the better fit when the data lives in NAS environments or object storage across multiple vendors, the volume reaches petabytes or billions of files, the files lack schema and need metadata enrichment before they are usable, sensitive data must be identified and excluded before reaching AI tools, or the goal is to make data queryable in Snowflake or Databricks without moving the underlying files.
Most enterprises need both. ETL handles structured application data flowing into analytics. A platform such as Komprise Intelligent Data Management handles the unstructured data estate: indexing it across silos, classifying and enriching it, controlling what reaches AI pipelines, and exposing it to AI and analytics platforms.